Dataset Information

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions.

ABSTRACT: BACKGROUND:Mobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition. OBJECTIVE:The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data. METHODS:Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study. Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, location, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32). RESULTS:On average, sensor data were obtained for 55% (Android) and 45% (iOS) of scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%. Despite this relatively small reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organization that collects data, and perceived impact on privacy were identified as main factors for acceptability. CONCLUSIONS:Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of mobile phone sensor technology to be rigorously assessed.

SUBMITTER: Boonstra TW

PROVIDER: S-EPMC6090171 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions.

Boonstra Tjeerd W TW Nicholas Jennifer J Wong Quincy Jj QJ Shaw Frances F Townsend Samuel S Christensen Helen H

Journal of medical Internet research 20180730 7

<h4>Background</h4>Mobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition.<h4>Objective</h4>The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data.<h4>Methods</h4>Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature ...[more]

PMID: 30061092

Similar Datasets

Project description:BackgroundTracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures.ObjectiveThe objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern.MethodsAdults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants' geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1).ResultsThe mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812).ConclusionsThe use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers.

Project description:BackgroundApproximately 1 in 5 American adults experience mental illness every year. Thus, mobile phone-based mental health prediction apps that use phone data and artificial intelligence techniques for mental health assessment have become increasingly important and are being rapidly developed. At the same time, multiple artificial intelligence-related technologies (eg, face recognition and search results) have recently been reported to be biased regarding age, gender, and race. This study moves this discussion to a new domain: phone-based mental health assessment algorithms. It is important to ensure that such algorithms do not contribute to gender disparities through biased predictions across gender groups.ObjectiveThis research aimed to analyze the susceptibility of multiple commonly used machine learning approaches for gender bias in mobile mental health assessment and explore the use of an algorithmic disparate impact remover (DIR) approach to reduce bias levels while maintaining high accuracy.MethodsFirst, we performed preprocessing and model training using the data set (N=55) obtained from a previous study. Accuracy levels and differences in accuracy across genders were computed using 5 different machine learning models. We selected the random forest model, which yielded the highest accuracy, for a more detailed audit and computed multiple metrics that are commonly used for fairness in the machine learning literature. Finally, we applied the DIR approach to reduce bias in the mental health assessment algorithm.ResultsThe highest observed accuracy for the mental health assessment was 78.57%. Although this accuracy level raises optimism, the audit based on gender revealed that the performance of the algorithm was statistically significantly different between the male and female groups (eg, difference in accuracy across genders was 15.85%; P<.001). Similar trends were obtained for other fairness metrics. This disparity in performance was found to reduce significantly after the application of the DIR approach by adapting the data used for modeling (eg, the difference in accuracy across genders was 1.66%, and the reduction is statistically significant with P<.001).ConclusionsThis study grounds the need for algorithmic auditing in phone-based mental health assessment algorithms and the use of gender as a protected attribute to study fairness in such settings. Such audits and remedial steps are the building blocks for the widespread adoption of fair and accurate mental health assessment algorithms in the future.

Project description:BackgroundDengue is a systemic and dynamic disease with symptoms ranging from undifferentiated fever to dengue shock syndrome. Assessment of patients' severity of dehydration is integral to appropriate care and management. Urine colour has been shown to have a high correlation with overall assessment of hydration status. This study tests the feasibility of measuring dehydration severity in dengue fever patients by comparing urine colour captured by mobile phone cameras to established laboratory parameters.Methodology/principal findingsPhotos of urine samples were taken in a customized photo booth, then processed using Adobe Photoshop to index urine colour into the red, green, and blue (RGB) colour space and assigned a unique RGB value. The RGB values were then correlated with patients' clinical and laboratory hydration indices using Pearson's correlation and multiple linear regression. There were strong correlations between urine osmolality and the RGB of urine colour, with r = -0.701 (red), r = -0.741 (green), and r = -0.761 (blue) (all p-value <0.05). There were strong correlations between urine specific gravity and the RGB of urine colour, with r = -0.759 (red), r = -0.785 (green), and r = -0.820 (blue) (all p-value <0.05). The blue component had the highest correlations with urine specific gravity and urine osmolality. There were moderate correlations between RGB components and serum urea, at r = -0.338 (red), -0.329 (green), -0.360 (blue). In terms of urine biochemical parameters linked to dehydration, multiple linear regression studies showed that the green colourimetry code was predictive of urine osmolality (β coefficient -0.082, p-value <0.001) while the blue colourimetry code was predictive of urine specific gravity (β coefficient -2,946.255, p-value 0.007).Conclusions/significanceUrine colourimetry using mobile phones was highly correlated with the hydration status of dengue patients, making it a potentially useful hydration status tool.

Project description:BackgroundHealth information technologies (HITs) are becoming increasingly recognized for their potential to provide innovative solutions to improve the delivery of mental health services and drive system reforms for better outcomes.ObjectiveThis paper describes the baseline results of a study designed to systematically monitor and evaluate the impact of implementing an HIT, namely the InnoWell Platform, into Australian mental health services to facilitate the iterative refinement of the HIT and the service model in which it is embedded to meet the needs of consumers and their supportive others as well as health professionals and service providers.MethodsData were collected via web-based surveys, semistructured interviews, and a workshop with staff from the mental health services implementing the InnoWell Platform to systematically monitor and evaluate its impact. Descriptive statistics, Fisher exact tests, and a reliability analysis were used to characterize the findings from the web-based surveys, including variability in the results between the services. Semistructured interviews were coded using a thematic analysis, and workshop data were coded using a basic content analysis.ResultsBaseline data were collected from the staff of 3 primary youth mental health services (n=18), a counseling service for veterans and their families (n=23), and a helpline for consumers affected by eating disorders and negative body image issues (n=6). As reported via web-based surveys, staff members across the services consistently agreed or strongly agreed that there was benefit associated with using technology as part of their work (38/47, 81%) and that the InnoWell Platform had the potential to improve outcomes for consumers (27/45, 60%); however, there was less certainty as to whether their consumers' capability to use technology aligned with how the InnoWell Platform would be used as part of their mental health care (11/45, 24% of the participants strongly disagreed or disagreed; 15/45, 33% were neutral; and 19/45, 42% strongly agreed or agreed). During the semistructured interviews (n=3) and workshop, participants consistently indicated that the InnoWell Platform was appropriate for their respective services; however, they questioned whether the services' respective consumers had the digital literacy required to use the technology. Additional potential barriers to implementation included health professionals' digital literacy and service readiness for change.ConclusionsDespite agreement among participants that HITs have the potential to result in improved outcomes for consumers and services, service readiness for change (eg, existing technology infrastructure and the digital literacy of staff and consumers) was noted to potentially impact the success of implementation, with less than half (20/45, 44%) of the participants indicating that their service was ready to implement new technologies to enhance mental health care. Furthermore, participants reported mixed opinions as to whether it was their responsibility to recommend technology as part of standard care.

Project description:BACKGROUND:Approximately 11,000 people die in Scotland each year as a result of smoking-related causes. Quitting smoking is relatively easy; maintaining a quit attempt is a very difficult task with success rates for unaided quit attempts stubbornly remaining in the single digits. Pharmaceutical treatment can improve these rates by lowering the overall reward factor of nicotine. However, these and related nicotine replacement therapies do not operate on, or address, the spatial and contextual aspects of smoking behaviour. With the ubiquity of smartphones that can log spatial, quantitative and qualitative data related to smoking behaviour, there exists a person-centred clinical opportunity to support smokers attempting to quit by first understanding their smoking behaviour and subsequently sending them dynamic messages to encourage health behaviour change within a situational context. METHODS:We have built a smartphone app-MapMySmoke-that works on Android and iOS platforms. The deployment of this app within a clinical National Health Service (NHS) setting has two distinct phases: (1) a 2-week logging phase where pre-quit patients log all of their smoking and craving events; and (2) a post-quit phase where users receive dynamic support messages and can continue to log craving events, and should they occur, relapse events. Following the initial logging phase, patients consult with their general practitioner (GP) or healthcare provider to review their smoking patterns and to outline a precise, individualised quit attempt plan. Our feasibility study consists of assessment of an initial app version during and after use by eight patients recruited from an NHS Fife GP practice. In addition to evaluation of the app as a potential smoking cessation aid, we have assessed the user experience, technological requirements and security of the data flow. RESULTS:In an initial feasibility study, we have deployed the app for a small number of patients within one GP practice in NHS Fife. We recruited eight patients within one surgery, four of whom actively logged information about their smoking behaviour. Initial feedback was very positive, and users indicated a willingness to log their craving and smoking events. In addition, two out of three patients who completed follow-up interviews noted that the app helped them reduce the number of cigarettes they smoked per day, while the third indicated that it had helped them quit. The study highlighted the use of pushed notifications as a potential technology for maintaining quit attempts, and the security of collection of data was audited. These initial results influenced the design of a planned second larger study, comprised of 100 patients, the primary objectives of which are to use statistical modelling to identify times and places of probable switches into smoking states, and to target these times with dynamic health behaviour messaging. CONCLUSIONS:While the health benefits of quitting smoking are unequivocal, such behaviour change is very difficult to achieve. Many factors are likely to contribute to maintaining smoking behaviour, yet the precise role of cues derived from the spatial environment remains unclear. The rise of smartphones, therefore, allows clinicians the opportunity to better understand the spatial aspects of smoking behaviour and affords them the opportunity to push targeted individualised health support messages at vulnerable times and places. TRIAL REGISTRATION:ClinicalTrial.gov, NCT02932917.

Project description:BackgroundCall detail records (CDRs) are collected by mobile network operators in the course of providing their service. CDRs are increasingly being used in research along with other forms of big data and represent an emerging data type with potential for public good. Many jurisdictions have infrastructures for health data research that could benefit from the integration of CDRs with health data.ObjectiveThe objective of this study was to review how CDRs have been used in health research and to identify challenges and potential opportunities for their wider use in conjunction with health data.MethodsA literature review was conducted using structured search terms making use of major search engines. Initially, 4066 items were identified. Following screening, 46 full text articles were included in the qualitative synthesis. Information extracted included research topic area, population of study, datasets used, information governance and ethical considerations, study findings, and data limitations.ResultsThe majority of published studies were focused on low-income and middle-income countries. Making use of the location element in CDRs, studies often modeled the transmission of infectious diseases or estimated population movement following natural disasters with a view to implementing interventions. CDRs were used in anonymized or aggregated form, and the process of gaining regulatory approvals varied with data provider and by jurisdiction. None included public views on the use of CDRs in health research.ConclusionsDespite various challenges and limitations, anonymized mobile phone CDRs have been used successfully in health research. The use of aggregated data is a safeguard but also a further limitation. Greater opportunities could be gained if validated anonymized CDRs were integrated with routine health records at an individual level, provided that permissions and safeguards could be put in place. Further work is needed, including gaining public views, to develop an ethically founded framework for the use of CDRs in health research.

Dataset Information

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions.

Publications

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets